Inflation is an important measure of any country’s economy, and the Retail Price Index (RPI) is one of the most widely used indicators in the United Kingdom, with the rate expected to have reached an annual average of 4.3 percent in 2025, compared with 3.6 percent in 2024. This followed 2022, when RPI inflation reached a rate of 11.6 percent, by far the highest annual rate during this provided time period. CPI vs RPI Although the Retail Price Index is a commonly utilized inflation indicator, the UK also uses a newer method of calculating inflation, the Consumer Price Index. The CPI, along with the CPIH (Consumer Price Index including owner occupiers' housing costs) are usually preferred by the UK government, but the RPI is still used in certain instances. Increases in rail fares for example, are calculated using the RPI, while increases in pension payments are calculated using CPI, when this is used as the uprating factor. The use of one inflation measure over the other can therefore have a significant impact on people’s lives in the UK. High inflation eases in 2024 Like the Retail Price Index, the Consumer Price Index inflation rate also reached a recent peak in October 2022. In that month, prices were rising by 11.1 percent and did not fall below double figures until April 2023. This fall was largely due to slower price increases in key sectors such as energy, which drove a significant amount of the 2022 wave of inflation. Inflation nevertheless remains elevated, fueled not only by high food inflation, but also by underlying core inflation. As of February 2025, the overall CPI inflation rate was 2.8 percent, although an uptick in inflation is expected later in the year, with a rate of 3.7 percent forecast for the third quarter of the year.

The 'Index-linked Treasury Gilt 2031 Auction' in the United Kingdom is an event where the government issues bonds that are linked to inflation, specifically the Retail Price Index (RPI).-2025-12-02

Philippines RPI: Chemicals: Paints, Varnishes and Related Compound data was reported at 992.810 1978=100 in Sep 2009. This records a decrease from the previous number of 993.020 1978=100 for Aug 2009. Philippines RPI: Chemicals: Paints, Varnishes and Related Compound data is updated monthly, averaging 595.570 1978=100 from Jan 1990 (Median) to Sep 2009, with 237 observations. The data reached an all-time high of 1,002.310 1978=100 in Apr 2009 and a record low of 375.250 1978=100 in Jan 1990. Philippines RPI: Chemicals: Paints, Varnishes and Related Compound data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.I059: Retail Price Index: 1978=100: Metro Manila.

Linked Data derived from datasets listed in http://data.melagrid.org. Linked Data URIs will have base URI http://lod.melagrid.org. Project is version controlled on github at https://github.com/jimmccusker/melagrid.

This dataset contains the known hydrocarbon reservoirs within the study area of the Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) as part of Phase 1, Natural Reservoirs Quality Analysis. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are included. RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal. Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf uploaded in the associated "Natural Reservoir Analysis" dataset linked below.

Germany RPI: 2000=100: SF: Motorcycles & Related Parts data was reported at 111.300 2000=100 in Mar 2009. This records an increase from the previous number of 111.100 2000=100 for Feb 2009. Germany RPI: 2000=100: SF: Motorcycles & Related Parts data is updated monthly, averaging 97.800 2000=100 from Jan 1991 (Median) to Mar 2009, with 219 observations. The data reached an all-time high of 111.300 2000=100 in Mar 2009 and a record low of 84.400 2000=100 in Jan 1991. Germany RPI: 2000=100: SF: Motorcycles & Related Parts data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.I056: Retail Price Index: 2000=100.

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The Prices Survey Microdata include the underlying price data used by the Office for National Statistics (ONS) to produce the Consumer Prices Index (CPI), the Retail Prices Index (RPI) and associated price indices. The CPI has become the main domestic measure of inflation for macroeconomic purposes in the UK. Since December 2003 it has been used for the inflation target that the Bank of England is required to achieve. The RPI is the most long-standing measure of inflation in the UK, and its uses have included the indexation of pensions, state benefits and index-linked gilts. The study also includes the data underlying the Producer Prices Index.

There are four levels of sampling for local price collection: locations/shopping areas; outlets/shops within locations; representative items/goods and services; and products and varieties (price quotes).

There are two basic price collection methods: local and central. Local collection is used for most items; prices are obtained from outlets in about 150 locations around the country. Some 110,000 quotations are obtained by this method. Normally, collectors must visit the outlet, but prices for some items may be collected by telephone. Central collection is used for items where all the prices can be collected centrally by the ONS with no field work. These prices can be further sub-divided into two categories, depending on their subsequent use: 1) central shops, where the prices are combined with prices obtained locally, and 2) central items, where the prices are used on their own to construct centrally calculated indices. There are about 130 items for which the prices are collected centrally.

The retail price data include the locations containing the shopping outlets from which the price quotes were obtained. These locations are intended to be broadly representative of a central shopping area and the areas where the local shopping population tend to live. The data also include the regions in which those shopping areas are located.

Linking to other business studies
The producer prices data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest edition information
For the 36th edition (September 2025), monthly Item Indices and Price Quotes data files for April to October 2024, and accompanying variable catalogues, have been added to the study.

The data are related to Figure 7 of the publication Blatnik et al. (2022) Late blight resistance conferred by Rpi-Smira2/R8 in potato genotypes in vitro depends on the genetic background, published in Plants 11: 1319 (https://doi.org/10.3390/plants11101319)

The data represent late blight (Phytophtora infestans) disease scores of progeny R8 genotypes and parental cultivars inoculated with four P. infestans isolates in vitro. The disease scores were evaluated daily for an eight day period post inoculation according to the late blight disease rating scale (see publication and info sheet of the data).

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Hong Kong (DC)RPI: CPI(A): Miscellaneous Social and Related Comm. data was reported at 81.900 Mar1994=100 in Mar 1998. This records an increase from the previous number of 78.200 Mar1994=100 for Dec 1997. Hong Kong (DC)RPI: CPI(A): Miscellaneous Social and Related Comm. data is updated quarterly, averaging 83.800 Mar1994=100 from Sep 1995 (Median) to Mar 1998, with 11 observations. The data reached an all-time high of 105.800 Mar1994=100 in Mar 1997 and a record low of 77.700 Mar1994=100 in Sep 1997. Hong Kong (DC)RPI: CPI(A): Miscellaneous Social and Related Comm. data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.G103: Payroll Indices: 1st Quarter 1994=100: by Industry: HSIC 1.1 (Discontinued).

We present a high-resolution magnetostratigraphy and relative paleointensity (RPI) record derived from the upper 85 meters of IODP Site U1336, an equatorial Pacific early to middle Miocene succession recovered during Expedition 320/321. The magnetostratigraphy is well resolved with reversals typically located to within a few centimeters resulting in a well-constrained age model. The lowest normal polarity interval, from 85 to 74.87 meters, is interpreted as the upper part of Chron C6n (18.614-19.599 Ma). Another 33 magnetozones occur from 74.87 to 0.85 m, which are interpret to represent the continuous sequence of chrons from Chron C5Er (18.431-18.614 Ma) up to the top of Chron C5An.1n (12.014 Ma). We identify three new possible subchrons within Chron C5Cn.1n, Chron 5Bn.1r, and C5ABn. Sedimentation rates vary from about 7 to 15 m/Myr with a mean of about 10 m/Myr. We observe rapid, apparent changes in the sedimentation rate at geomagnetic reversals between ~16 and 19 Ma that indicate a calibration error in geomagnetic polarity timescale (ATNTS2004). The remanence is carried mainly by non-interacting particles of fine-grained magnetite, which have FORC distributions characteristic of biogenic magnetite. Given the relative homogeneity of the remanence carriers throughout the 85-m-thick succession and the quality with which the remanence is recorded, we have constructed a relative paleointensity (RPI) record that provides new insights into middle Miocene geomagnetic field behavior. The RPI record indicates a gradual decline in field strength between 18.5 Ma and 14.5 Ma, and indicates no discernible link between RPI and either chron duration or polarity state.

Philippines RPI: MM: Mineral Fuels, Lubricants & Related Materials data was reported at 311.700 2000=100 in Sep 2018. This records an increase from the previous number of 305.000 2000=100 for Aug 2018. Philippines RPI: MM: Mineral Fuels, Lubricants & Related Materials data is updated monthly, averaging 233.900 2000=100 from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 327.000 2000=100 in Apr 2011 and a record low of 90.700 2000=100 in Jan 2000. Philippines RPI: MM: Mineral Fuels, Lubricants & Related Materials data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.I058: Retail Price Index: 2000=100: Metro Manila.

Definition of Hydrographic Unit based on the Sander: Environmental variable for calculating the RPI (River Fish Index) and relating to a territorial area that has been demarcated according to faunistic criteria. The perimeter concerns 17 municipalities, i.e. 24 680 ha.

The files included in this submission contain all data pertinent to the methods and results of this task's output, which is a cohesive multi-state map of all known potential geothermal reservoirs in our region, ranked by their potential favorability. Favorability is quantified using a new metric, Reservoir Productivity Index, as explained in the Reservoirs Methodology Memo (included in zip file). Shapefile and images of the Reservoir Productivity and Reservoir Uncertainty are included as well (hover over file display names to see actual file names in bottom-left corner of screen). This shapefile contains the data associated with the GPFA-AB Phase 1 Task 2, Natural Reservoirs Quality Analysis, in a format that can be uploaded into any GIS software. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are held in columns "RPI" and "RPI CV". RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal.Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf. *Newer version exists - see link below

Comprehensive database of time series covering measures of inflation data for the UK including CPIH, CPI and RPI.

Abstract: The data set contains major oxide data for MORB glasses from the Gakkel Ridge collected on the HY0102 and PS59 cruises, measured at RPI by microprobe.

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare.Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods.Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events.Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Definition of Hydrographic Unit based on the Sander: environmental variable allowing the calculation of the RPI and relating to a territorial area which has been demarcated according to faunistic criteria.

Mass spectrometry data for the DERA-RPI complex submission related to Supplemental Figure S3 in Dwyer et. al., Communications Biology.